{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a5a034ef",
   "metadata": {},
   "source": [
    "### 脉冲响应"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80813cb0",
   "metadata": {},
   "source": [
    "与matlab中的filter函数相对应，在python中scipy库提供了求解滤波器幅频响应的函数，可以方便的用该函数来求系统的脉冲响应"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a0f0ccbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy import signal\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "29ccc3a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "N = 50\n",
    "t = np.linspace(0,50,50)\n",
    "xn = signal.unit_impulse(N)\n",
    "b = np.array([1])\n",
    "a = np.array([1.0, -0.9])\n",
    "zi = signal.lfilter_zi(b, a)\n",
    "y, _ = signal.lfilter(b, a, xn, zi=zi*xn[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "36860904",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "ax.stem(t, y)\n",
    "fig.savefig('脉冲响应（离散）.png',dpi=500)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72125e39",
   "metadata": {},
   "source": [
    "也可以输出为连续的曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "55b89922",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "ax.plot(t, y)\n",
    "fig.savefig('脉冲响应曲线（连续）.png',dpi=500)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
